An idiot's guide (my personal notes, to myself).

Scaling AI often means scaling cost, complexity, and confusion. More models, more APIs, more prompts, more "solutions" that don't join up. The goal shouldn't be to use more AI. It should be to get more value from the same or less.

Start with the bottleneck. Before adding another tool or another integration, ask what's actually limiting us. Is it data quality, process design, or something that will cost in AI usage? Fix the system first, then decide if and where AI fits.

Reuse before you scale. One well-designed prompt, one clear workflow, one shared pattern that works is worth more than a dozen one-off experiments. Document what works. Use it in the next project. Scale the pattern, not the pile of ad-hoc scripts. Think of Agentic and prompts as job descriptions and processes. You're designing a department with LLMs, not something magical.

Measure what you care about. If you can't say what "success" looks like in concrete terms, you're not scaling. You're spreading. Define the outcome (time saved, error rate, decision quality), measure it before and after, and only then scale.

Kill waste early. Pilot on small scope. If it doesn't move the needle, stop. If it does, grow deliberately. The biggest waste in scaling AI is scaling something that never deserved to leave the lab. It is okay to kill an idea in discovery, this is what discovery is for.